Background Information: We were tasked by Kirk Bogard, the Associate Vice President for Development and External Relations at Miami University to explore a dataset of real student data in order to find relationships and patterns that he can use to give Miami a competitive advantage. After exploring the data, we found a few particular variables that can help us find a potential relationship in the dataset, survey_salary, survery_internships, and survey_state. We plan to build a regression model using the number of internships during college to predict salary after graduation, using state to control for salary. The purpose of this analysis is to provide information on the relationship between number of internships and salary information to FSB Career Services. This will help them give more accurate guidance to students to ensure they get the best full time opportunity for them.
Survey Overview:
Internship Effects on Salary:
External Research:
Overview of survey responses
rate = round(100 * nrow(df)/nrow(df2),0)
gauge(rate, min=0, max=100, symbol='%', gaugeSectors(
success=c(80,100), warning= c(40,79), danger=c(0,39)
)
)
Call:
lm(formula = df$survey_salary ~ df$survey_internships)
Residuals:
Min 1Q Median 3Q Max
-51384 -6755 245 5745 115713
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51511.7 970.6 53.071 < 2e-16 ***
df$survey_internships1 5243.0 1086.3 4.826 1.51e-06 ***
df$survey_internships2 7775.3 1065.8 7.296 4.52e-13 ***
df$survey_internships3 9872.5 1220.7 8.088 1.14e-15 ***
df$survey_internships4 9893.8 2207.3 4.482 7.87e-06 ***
df$survey_internships5 9388.3 4258.6 2.205 0.0276 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11730 on 1722 degrees of freedom
Multiple R-squared: 0.04643, Adjusted R-squared: 0.04366
F-statistic: 16.77 on 5 and 1722 DF, p-value: 3.286e-16